Muhammad Imran Ahmad
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Preferred name
Muhammad Imran Ahmad
Official Name
Muhammad Imran, Ahmad
Alternative Name
Ahmad, Muhammad Imran
Ahmad, M. I.
Imran Ahmad, Muhammad
Ahmad, Muhamad Imran
Main Affiliation
Scopus Author ID
57214845678
Researcher ID
GBE-1471-2022

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  • Publication
    Durio Zibethinus L Plantation Intelligent Web-Based Irrigation System using fuzzy logic (DuWIMS) based on IoT
    (Semarak Ilmu Publishing, 2025-07)
    Zahari Awang Ahmad
    ;
    Muhammad Imran Ahmad
    ;
    Shuhaizar Daud
    In response to the challenges posed by the recent pandemic on traditional farming in Malaysia, this paper proposes DuWIMS, an innovative smart irrigation system that integrates a modern web interface with an IoT-based system. Utilizing an array of sensors for real-time data collection, the system enhances irrigation efficiency through a dual-mode operation: manual remote control and automated irrigation via a fuzzy logic model. This system, tested on a durian farm in Kedah, Malaysia, significantly reduced irrigation time from two hours to approximately 50 minutes for 65 trees and decreased water usage by about 25%. Furthermore, durian trees irrigated with this system exhibited improved growth compared to those under traditional irrigation methods. Operational efficiency was bolstered with remote irrigation control and reliable performance, as evidenced by no system downtime over a three-month period. The deployment of the fuzzy logic model on a cloud server, serving as a backend API, also proved cost-effective by negating the need for on-site computing units. These findings underscore the potential of DuWIMS to revolutionize agricultural practices, particularly in challenging times, by optimizing resource usage and promoting sustainable farming.
  • Publication
    IoT Enabled Mushroom Farm Automation with Machine Learning
    (Universiti Malaysia Perlis, 2024-06-03)
    Shafie Omar
    ;
    Wan Mohd Faizal Wan Nik
    ;
    Muhammad Imran Ahmad
    ;
    Tan Shie Chow
    ;
    Mohd Nazri Abu Bakar
    ;
    Shahrul Fazly Man@Sulaiman
    ;
    Fadhilnor Abdullah
    ;
    Vikneshwara Ram Suppiah
    Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.
      26  3
  • Publication
    AI Assisted and IOT Based Fertilizer Mixing System
    (Universiti Malaysia Perlis, 2024-06-03)
    Wan Mohd Faizal Wan Nik
    ;
    Shahrul Fazly Man@Sulaiman
    ;
    Muhammad Imran Ahmad
    ;
    Shafie Omar
    ;
    Tan Shie Chow
    ;
    Mohd Nazri Abu Bakar
    ;
    Fadhilnor Abdullah
    ;
    Muhammad Khamil Akbar
    Agriculture techniques, particularly fertilizer mixing, have significant impacts on crop productivity. Introducing IoT technology to agriculture can enhance productivity, and machine learning offers a mechanism to gain insights from data, making agricultural practices more efficient. This research aims to design an AI-assisted and IoT-based fertilizer mixing system for greenhouses. This system utilizes sensor data and AI algorithms, specifically the Support Vector Machine (SVM), to optimize fertilizer application. Results from the SVM classifier showed a 100% accuracy rate for temperature and humidity, 65% accuracy for phosphorus, 86% for nitrogen, and 100% for potassium. These findings demonstrate the potential of the proposed system to improve fertilizer efficiency while reducing labor and resource waste.
      2  18